This section introduces aspects that may be helpful in facilitating a better understanding of the systems and methods disclosed herein. Accordingly, the statements of this section are to be read in this light and are not to be understood or interpreted as admissions about what is or is not in the prior art.
Digital image/video cameras acquire and process a significant amount of raw data. In order to store or transmit image data efficiently, the raw pixel data for each of the N pixels of an N-pixel image is first captured and then typically compressed using a suitable compression algorithm for storage and/or transmission. Although compression after capturing the raw data for each of the N pixels of the image is generally useful for reducing the size of the image (or video) captured by the camera, it requires significant computational resources and time. In addition, compression of the raw pixel data does not always meaningfully reduce the size of the captured images.
A more recent approach, known as compressive sense imaging, acquires compressed image (or video) data using random projections without first collecting the raw data for all of the N pixels of an N-pixel image. For example, a compressive measurement basis is applied to obtain a series of compressive measurements which represent the encoded (i.e., compressed) image. Since a reduced number of compressive measurements are acquired in comparison to the raw data for each of the N pixel values of a desired N-pixel image, this approach can significantly eliminate or reduce the need for applying compression after the raw data is captured.